One of the effective ways to improve the operational efficiency of the rail system and achieve intelligent operation of rail transit, based on machine learning algorithm theory, combined with the passenger flow characteristics of rail transit stations such as time, space, and external influencing factors, a lightweight gradient boosting machine (LightGBM), long short term memory (LSTM), and LightGBM-LSTM fusion model for station short-term passenger flow prediction are established, Simultaneously constructing differential autoregressive integrated moving average (ARIMA) and extreme gradient boosting (XGBoost) models as control models for predictive experiments. Taking the swiping data of the Hangzhou subway ticketing system in China as an example, Five subway stations (residential type, work type, mixed residential and work type, shopping type, and transportation hub type) and three accuracy evaluation indicators (average absolute error, root mean square error, and average absolute percentage error) were selected to quantitatively evaluate the predictive accuracy of different models
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